분석 Python/Tensorflow
TensorFlow gpu cuda 설치 공식 문서 (Windows / Ubuntu 16.04 ,18.04)
데이터분석뉴비
2019. 10. 3. 22:16
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Ubuntu 16.04 (CUDA 10)
# Add NVIDIA package repositories
# Add HTTPS support for apt-key
sudo apt-get install gnupg-curl
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_10.0.130-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt-get update
# Install NVIDIA driver
# Issue with driver install requires creating /usr/lib/nvidia
sudo mkdir /usr/lib/nvidia
sudo apt-get install --no-install-recommends nvidia-driver-418
# Reboot. Check that GPUs are visible using the command: nvidia-smi
# Install development and runtime libraries (~4GB)
sudo apt-get install --no-install-recommends \
cuda-10-0 \
libcudnn7=7.6.2.24-1+cuda10.0 \
libcudnn7-dev=7.6.2.24-1+cuda10.0
# Install TensorRT. Requires that libcudnn7 is installed above.
sudo apt-get install -y --no-install-recommends libnvinfer5=5.1.5-1+cuda10.0 \
libnvinfer-dev=5.1.5-1+cuda10.0
Ubuntu 16.04 (CUDA 9.0 for TensorFlow < 1.13.0)
# Add NVIDIA package repository
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt install ./cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt update
# Install the NVIDIA driver
# Issue with driver install requires creating /usr/lib/nvidia
sudo mkdir /usr/lib/nvidia
sudo apt-get install --no-install-recommends nvidia-410
# Reboot. Check that GPUs are visible using the command: nvidia-smi
# Install CUDA and tools. Include optional NCCL 2.x
sudo apt install cuda9.0 cuda-cublas-9-0 cuda-cufft-9-0 cuda-curand-9-0 \
cuda-cusolver-9-0 cuda-cusparse-9-0 libcudnn7=7.2.1.38-1+cuda9.0 \
libnccl2=2.2.13-1+cuda9.0 cuda-command-line-tools-9-0
# Optional: Install the TensorRT runtime (must be after CUDA install)
sudo apt update
sudo apt install libnvinfer4=4.1.2-1+cuda9.0
https://www.tensorflow.org/install/gpu?hl=ko
GPU support | TensorFlow
Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards. TensorFlow GPU support requires an assortment of drivers and libraries. To simplify installation and avoid library conflicts, we recommend using a TensorFlow Docker image with
www.tensorflow.org
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